IEEE CogMI 2020

Machine Learning Systems in the IoT: Trustworthiness Trade-offs for Edge Intelligence 

Abstract:

Machine learning systems (MLSys) are emerging in the 
Internet of Things (IoT) to provision edge intelligence, 
which is paving our way towards the vision of ubiquitous 
intelligence. However, despite the maturity of machine 
learning systems and the IoT, we are facing severe 
challenges when integrating MLSys and IoT in practical 
context. For instance, many machine learning systems 
have been developed for large-scale production (e.g., 
cloud environments), but IoT introduces additional 
demands due to heterogeneous and resource-constrained 
devices and decentralized operation environment. To shed 
light on this convergence of MLSys and IoT, this paper 
analyzes the trade-offs by covering the latest developments 
(up to 2020) on scaling and distributing ML across cloud, 
edge, and IoT devices. We position machine learning 
systems as a component of the IoT, and edge intelligence 
as a socio-technical system. On the challenges of designing 
trustworthy edge intelligence, we advocate a holistic 
design approach that takes multi-stakeholder concerns, 
design requirements and trade-offs into consideration, 
and highlight the future research opportunities in edge 
intelligence.


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BibTeX:
@INPROCEEDINGS{Toussaint:CogMI2020, 
author={V. {Toussaint} and A. Y. {Ding}}, 
booktitle={2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI)}, 
title={Machine Learning Systems in the IoT: Trustworthiness Trade-offs for Edge Intelligence},
year={2020},
pages={177-184},
doi={10.1109/CogMI50398.2020.00030},
}
How to cite:

V. Toussaint, A. Y. Ding, "Machine Learning Systems in the IoT: Trustworthiness Trade-offs for Edge Intelligence", in Proceedings of the Second IEEE International Conference on Cognitive Machine Intelligence (CogMI), pp. 177-184, 2020. DOI: 10.1109/CogMI50398.2020.00030